The GPI report evaluates 163 countries that account for more than 99.7% of the world’s total population. The factors analyzed in the report are grouped into three different areas: Safety and Security, Ongoing Conflict, and Militarization. The factors used to compile this report include: the number of internal and external violent conflicts, level of distrust, politicla instability, potential for terrorist acts, number of homicides, and military expenditures as a percetnage of GDP. A score is calculated for each of the 163 nations featured in the report based on these factors. The higher the score, the more dangerous the country is and the lower it ranks in terms of safety.
Top 20 Most Dangerous Countries in the World (Global Peace Index 2022 — higher scores are less safe)
Ref: https://international.ipums.org/international/index.shtmllibrary(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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library(tmap)
library(tmaptools)
library(sf)## Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUElibrary(rgdal)## Loading required package: sp
## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
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## rgdal: version: 1.5-32, (SVN revision 1176)
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## Loaded GDAL runtime: GDAL 3.4.3, released 2022/04/22
## Path to GDAL shared files: C:/Users/jim_PC_dell/AppData/Local/R/win-library/4.2/rgdal/gdal
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library(stats)
library(spdplyr)## Warning: package 'spdplyr' was built under R version 4.2.2library(tigris)## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.library(mapview)
library(DT)Ref:https://worldpopulationreview.com/country-rankings/most-dangerous-countries
Take a quick look at the whole dangerous country index
Some country names are different from the IPUMS Country shapefiles and will meed modificationcountry_10 <- read_csv("data/csvData.csv") %>%
select(c(1:3))## Rows: 162 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): country
## dbl (5): gpi22, dangerRank22, gpi21, dangerRank21, gpiChange
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.datatable(country_10) Rename some variables
Filter to 10 countries change variables to numeric classcountry_10 <- read_csv("data/csvData.csv") %>%
select(c(1:3)) %>%
rename(score = gpi22, rank = dangerRank22, CNTRY_NAME = country) %>%
filter(rank %in% c(1:10)) ## Rows: 162 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): country
## dbl (5): gpi22, dangerRank22, gpi21, dangerRank21, gpiChange
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.country_10$score <- as.numeric(country_10$score)
country_10 <- country_10 %>% mutate(Danger.Score = as.numeric(score * 1000))# countrys_map from https://international.ipums.org/international/index.shtml
country_sf <- sf::st_read("shp/world_countries_2020.shp") %>%
filter(CNTRY_NAME %in% c( "Afghanistan", "Yemen", "Syria", "Russia", "South Sudan", "Congo", "Iraq", "Somalia", "Central Africa Republic", "Sudan")|CNTRY_CODE == "140")## Reading layer `world_countries_2020' from data source
## `C:\Users\jim_PC_dell\Desktop\new R files\30 Day Map Challenge 2022\Master\Bad Countries\shp\world_countries_2020.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 284 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -89.99998 xmax: 180 ymax: 83.63339
## Geodetic CRS: WGS 84head(country_sf)## Simple feature collection with 6 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 11.15324 ymin: -5.031389 xmax: 74.88986 ymax: 38.49073
## Geodetic CRS: WGS 84
## OBJECTID CNTRY_NAME CNTRY_CODE BPL_CODE
## 1 11 Central African Republic 140 12030
## 2 18 Congo 178 12050
## 3 54 Somalia 706 11140
## 4 55 Sudan 729 13050
## 5 58 South Sudan 728 11150
## 6 70 Afghanistan 004 32010
## geometry
## 1 MULTIPOLYGON (((22.5812 11....
## 2 MULTIPOLYGON (((17.46837 3....
## 3 MULTIPOLYGON (((41.58164 -1...
## 4 MULTIPOLYGON (((38.49294 18...
## 5 MULTIPOLYGON (((33.28475 12...
## 6 MULTIPOLYGON (((71.00445 38...ctry_combined_sf <- merge(country_sf, country_10, duplicateGeoms = T)tmap_mode("view") ## tmap mode set to interactive viewingtm_shape(ctry_combined_sf) +
tm_basemap("Esri.WorldTopoMap") +
tm_layout("2022 Bad Map - Top 10 Most
Dangerous per Institute for Economics and Peace") +
tm_polygons(col = "Danger.Score", palette = c("#E3735E", "#EC5800", "#C70039"),
style = "fixed",
breaks = c(3000, 3100, 3200, 3300, 3400, 3500, 3600),
legend.hist = TRUE,
popup.vars = TRUE)